Performance of a pharmaceutical services regionalization strategy policy in Minas Gerais, Brazil: Pre-post analysis from ERAF project
Bibliographic record
Abstract
Background: In 2016, the Brazilian state of Minas Gerais (∼20 million people), implemented the ERAF policy (“Regionalization Strategy of Pharmaceutical Services”) in an effort to improve medicine procurement and distribution within primary care. We evaluated the impact of the policy on three main goals: price reductions, volume increases, and expansion of therapeutic options. Methods: We analyzed the procurement data from the Integrated System of Management of Pharmaceutical Services database in 2012 and 2018. We estimated the volume, drug mix, and expenditure indicators for all major therapeutic classes, and, in detail, for cardiovascular and nervous system drugs. We evaluated the expenditure drivers using decomposition analyses. Results: Overall, the expenditure increased by 14.5%, drug mix almost doubled, while the volume decreased by a third. Cardiovascular and neurological system drugs followed similar patterns. Decomposition analyses showed that prices and drug mix had positive effects while the volume had negative effects, resulting in an overall increase in expenditure. Conclusion: Our findings suggest that the ERAF policy cannot be considered effective as it has not fulfilled its intended purposes so far. Strategies to address the identified problems and to build a platform for a more sustainable long-lasting policy should be put in place by the government.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".